Navigating the Roadblocks: National Patient and Provider Survey on Barriers to Healthcare and Medication Access for Patients With Vasculitis
Bibliographic record
Abstract
OBJECTIVE: Timely diagnosis, specialized care, and medication access are critical for managing vasculitis. This study quantified barriers to care reported by patients and healthcare providers (HCPs). METHODS: Two primarily quantitative surveys were disseminated from September 2022 to June 2023 to 100 patients with vasculitis and 31 HCPs, through the Vasculitis Foundation Canada and the Canadian Rheumatology Association. This study was a secondary descriptive analysis of the data to analyze patient and HCP perspectives on diagnostic delays, appointment access, and medication challenges. RESULTS: Diagnostic delays were common, with 66% of patients reporting initial misdiagnoses, and 35% consulting ≥ 5 doctors before receiving a diagnosis of vasculitis. Among those referred to rheumatology, 57% waited > 1 month for an appointment. HCPs cited a lack of family physicians (74%), long waitlists (58%), and inappropriate referrals (48%) as major barriers. Forty-four percent of patients reported challenges associated with medication use, particularly related to adverse effects, out-of-pocket costs, and limited insurance coverage. Eighty-three percent of patients reported hospital visits at least once for vasculitis-related symptoms, most commonly due to disease flare. Eighty percent of HCPs reported challenges with prescribing or accessing medications for vasculitis, including issues associated with prior authorizations and step therapy protocols. Rituximab was the most commonly mentioned medication associated with these challenges. CONCLUSION: This study identified substantial barriers to vasculitis care, including diagnostic delays and limited access to medications. Targeted interventions, such as improving referral pathways, expanding provider availability, and reducing administrative burdens, are essential to improving access for this vulnerable population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".